NBA High-Scorer Prediction - ML Pipeline

🎯 Project Overview

What: Built an ML pipeline that predicts high-scoring NBA players with 95%+ F1 score accuracy using 2023-2024 season data.

Why: Wanted to build a foundation in machine learning through a real-world application, combining my interest in basketball with structured statistical data.

Impact: Successfully predicted points scored while gaining hands-on experience with ML models and classification techniques.

🔧 Technical Implementation

• Stack: Python, scikit-learn, NBA Stats API, pandas, NumPy
• Models: Compared KNN, Linear Regression, Logistic Regression, and Linear with Elastic Net Regularization
• Pipeline: API data ingestion → feature engineering → dimensionality reduction → model training & evaluation

Logistic regression performed best, offering both high accuracy and interpretability. The model revealed interesting insights like negative correlations with blocks, suggesting a split between offensive and defensive player archetypes.

🚧 Key Challenges

Data Quality: The NBA Stats API returns comprehensive but noisy data. Built a preprocessing pipeline to filter outliers (players with minimal playing time) and remove irrelevant features that could introduce bias.

Model Selection: Systematically compared three approaches using cross-validation for both classification and regression. Logistic regression and elastic net regularization emerged as the optimal classifier and regressor respectivly, balancing accuracy with interpretability.

📊 Results & Impact

• Prediction Accuracy: 95%+ F1 score in classifying high-scoring players
• Model Insights: Discovered meaningful patterns and learned to recognize overfitting and failure modes through experience
• Technical Validation: Demonstrated ability to build quality ML pipelines from raw data to validated predictions

💡 Key Takeaways

Technical Skills: End-to-end ML pipeline development, classification algorithms, feature engineering, dimensionality reduction, API integration, data preprocessing

Professional Skills: Self-directed learning, systematic experimentation, model evaluation methodology, completing technical projects independently